Shiny App Experiment Lab
Libraries
library(here)
library(dplyr)
library(arrow)
library(tidyr)
library(geojsonio)
Reading data
census_dataset <- open_dataset(here("data", "processed", "parquet_data_coords"))
census_dataset
FileSystemDataset with 33 Parquet files
NOM_MUN: string
NOM_LOC: string
LONGITUD: string
LATITUD: string
POBTOT: double
REL_H_M: string
POB0_14: string
POB15_64: string
POB65_MAS: string
P_0A4: string
P_0A4_F: string
P_0A4_M: string
P_5A9: string
P_5A9_F: string
P_5A9_M: string
P_10A14: string
P_10A14_F: string
P_10A14_M: string
P_15A19: string
P_15A19_F: string
P_15A19_M: string
P_20A24: string
P_20A24_F: string
P_20A24_M: string
P_25A29: string
P_25A29_F: string
P_25A29_M: string
P_30A34: string
P_30A34_F: string
P_30A34_M: string
P_35A39: string
P_35A39_F: string
P_35A39_M: string
P_40A44: string
P_40A44_F: string
P_40A44_M: string
P_45A49: string
P_45A49_F: string
P_45A49_M: string
P_50A54: string
P_50A54_F: string
P_50A54_M: string
P_55A59: string
P_55A59_F: string
P_55A59_M: string
P_60A64: string
P_60A64_F: string
P_60A64_M: string
P_65A69: string
P_65A69_F: string
P_65A69_M: string
P_70A74: string
P_70A74_F: string
P_70A74_M: string
P_75A79: string
P_75A79_F: string
P_75A79_M: string
P_80A84: string
P_80A84_F: string
P_80A84_M: string
P_85YMAS: string
P_85YMAS_F: string
P_85YMAS_M: string
PROM_HNV: string
PNACENT: string
PNACENT_F: string
PNACENT_M: string
PNACOE: string
PNACOE_F: string
PNACOE_M: string
longitude_decimal: double
latitude_decimal: double
NOM_ENT: string
See $metadata for additional Schema metadata
Reading specific data
pueb_norm <- census_dataset |>
filter(NOM_ENT=="Puebla") |>
collect()
Warning: Invalid metadata$rWarning: Invalid metadata$rWarning: Invalid metadata$rWarning: Invalid metadata$r
pueb_norm
unique(pueb_norm$NOM_MUN)
[1] "Total de la entidad Puebla" "Acajete" "Acateno" "Acatlán"
[5] "Acatzingo" "Acteopan" "Ahuacatlán" "Ahuatlán"
[9] "Ahuazotepec" "Ahuehuetitla" "Ajalpan" "Albino Zertuche"
[13] "Aljojuca" "Altepexi" "Amixtlán" "Amozoc"
[17] "Aquixtla" "Atempan" "Atexcal" "Atlixco"
[21] "Atoyatempan" "Atzala" "Atzitzihuacán" "Atzitzintla"
[25] "Axutla" "Ayotoxco de Guerrero" "Calpan" "Caltepec"
[29] "Camocuautla" "Caxhuacan" "Coatepec" "Coatzingo"
[33] "Cohetzala" "Cohuecan" "Coronango" "Coxcatlán"
[37] "Coyomeapan" "Coyotepec" "Cuapiaxtla de Madero" "Cuautempan"
[41] "Cuautinchán" "Cuautlancingo" "Cuayuca de Andrade" "Cuetzalan del Progreso"
[45] "Cuyoaco" "Chalchicomula de Sesma" "Chapulco" "Chiautla"
[49] "Chiautzingo" "Chiconcuautla" "Chichiquila" "Chietla"
[53] "Chigmecatitlán" "Chignahuapan" "Chignautla" "Chila"
[57] "Chila de la Sal" "Honey" "Chilchotla" "Chinantla"
[61] "Domingo Arenas" "Eloxochitlán" "Epatlán" "Esperanza"
[65] "Francisco Z. Mena" "General Felipe Ángeles" "Guadalupe" "Guadalupe Victoria"
[69] "Hermenegildo Galeana" "Huaquechula" "Huatlatlauca" "Huauchinango"
[73] "Huehuetla" "Huehuetlán el Chico" "Huejotzingo" "Hueyapan"
[77] "Hueytamalco" "Hueytlalpan" "Huitzilan de Serdán" "Huitziltepec"
[81] "Atlequizayan" "Ixcamilpa de Guerrero" "Ixcaquixtla" "Ixtacamaxtitlán"
[85] "Ixtepec" "Izúcar de Matamoros" "Jalpan" "Jolalpan"
[89] "Jonotla" "Jopala" "Juan C. Bonilla" "Juan Galindo"
[93] "Juan N. Méndez" "Lafragua" "Libres" "La Magdalena Tlatlauquitepec"
[97] "Mazapiltepec de Juárez" "Mixtla" "Molcaxac" "Cañada Morelos"
[101] "Naupan" "Nauzontla" "Nealtican" "Nicolás Bravo"
[105] "Nopalucan" "Ocotepec" "Ocoyucan" "Olintla"
[109] "Oriental" "Pahuatlán" "Palmar de Bravo" "Pantepec"
[113] "Petlalcingo" "Piaxtla" "Puebla" "Quecholac"
[117] "Quimixtlán" "Rafael Lara Grajales" "Los Reyes de Juárez" "San Andrés Cholula"
[121] "San Antonio Cañada" "San Diego la Mesa Tochimiltzingo" "San Felipe Teotlalcingo" "San Felipe Tepatlán"
[125] "San Gabriel Chilac" "San Gregorio Atzompa" "San Jerónimo Tecuanipan" "San Jerónimo Xayacatlán"
[129] "San José Chiapa" "San José Miahuatlán" "San Juan Atenco" "San Juan Atzompa"
[133] "San Martín Texmelucan" "San Martín Totoltepec" "San Matías Tlalancaleca" "San Miguel Ixitlán"
[137] "San Miguel Xoxtla" "San Nicolás Buenos Aires" "San Nicolás de los Ranchos" "San Pablo Anicano"
[141] "San Pedro Cholula" "San Pedro Yeloixtlahuaca" "San Salvador el Seco" "San Salvador el Verde"
[145] "San Salvador Huixcolotla" "San Sebastián Tlacotepec" "Santa Catarina Tlaltempan" "Santa Inés Ahuatempan"
[149] "Santa Isabel Cholula" "Santiago Miahuatlán" "Huehuetlán el Grande" "Santo Tomás Hueyotlipan"
[153] "Soltepec" "Tecali de Herrera" "Tecamachalco" "Tecomatlán"
[157] "Tehuacán" "Tehuitzingo" "Tenampulco" "Teopantlán"
[161] "Teotlalco" "Tepanco de López" "Tepango de Rodríguez" "Tepatlaxco de Hidalgo"
[165] "Tepeaca" "Tepemaxalco" "Tepeojuma" "Tepetzintla"
[169] "Tepexco" "Tepexi de Rodríguez" "Tepeyahualco" "Tepeyahualco de Cuauhtémoc"
[173] "Tetela de Ocampo" "Teteles de Avila Castillo" "Teziutlán" "Tianguismanalco"
[177] "Tilapa" "Tlacotepec de Benito Juárez" "Tlacuilotepec" "Tlachichuca"
[181] "Tlahuapan" "Tlaltenango" "Tlanepantla" "Tlaola"
[185] "Tlapacoya" "Tlapanalá" "Tlatlauquitepec" "Tlaxco"
[189] "Tochimilco" "Tochtepec" "Totoltepec de Guerrero" "Tulcingo"
[193] "Tuzamapan de Galeana" "Tzicatlacoyan" "Venustiano Carranza" "Vicente Guerrero"
[197] "Xayacatlán de Bravo" "Xicotepec" "Xicotlán" "Xiutetelco"
[201] "Xochiapulco" "Xochiltepec" "Xochitlán de Vicente Suárez" "Xochitlán Todos Santos"
[205] "Yaonáhuac" "Yehualtepec" "Zacapala" "Zacapoaxtla"
[209] "Zacatlán" "Zapotitlán" "Zapotitlán de Méndez" "Zaragoza"
[213] "Zautla" "Zihuateutla" "Zinacatepec" "Zongozotla"
[217] "Zoquiapan" "Zoquitlán"
Example
extract_coordinates <- function(data, municipality, locality) {
selected_location <- data |>
filter(NOM_MUN == municipality, NOM_LOC == locality)
coordinates <- tibble(
long = selected_location$longitude_decimal,
lat = selected_location$latitude_decimal
)
return(coordinates)
}
municipality <- "Acajete"
locality <- "Santa Isabel Tepetzala"
red_point <- extract_coordinates(pueb_norm, municipality, locality) |>
slice(1)
# red_point <- data.frame(long = -98.2035, lat = 19.0414)
geojson_file <- geojson_read("../data/processed/mexico.geojson", what = "sp")
filtered_geojson <- geojson_file |>
filter(name == "Puebla")
ggplot() +
geom_polygon(data = filtered_geojson,
aes(x = long, y = lat, group = group),
fill = "lightgray", color = "white") +
geom_point(data = red_point, aes(x = long, y = lat), color = "red", size = 3) +
theme_void() +
coord_map()
Warning: `fortify(<SpatialPolygonsDataFrame>)` was deprecated in ggplot2 3.4.4.
Please migrate to sf.Regions defined for each Polygons

Population graph
pueb_norm
Getting column names
column_names <- names(pueb_norm)
column_names
[1] "NOM_MUN" "NOM_LOC" "LONGITUD" "LATITUD" "POBTOT" "REL_H_M" "POB0_14"
[8] "POB15_64" "POB65_MAS" "P_0A4" "P_0A4_F" "P_0A4_M" "P_5A9" "P_5A9_F"
[15] "P_5A9_M" "P_10A14" "P_10A14_F" "P_10A14_M" "P_15A19" "P_15A19_F" "P_15A19_M"
[22] "P_20A24" "P_20A24_F" "P_20A24_M" "P_25A29" "P_25A29_F" "P_25A29_M" "P_30A34"
[29] "P_30A34_F" "P_30A34_M" "P_35A39" "P_35A39_F" "P_35A39_M" "P_40A44" "P_40A44_F"
[36] "P_40A44_M" "P_45A49" "P_45A49_F" "P_45A49_M" "P_50A54" "P_50A54_F" "P_50A54_M"
[43] "P_55A59" "P_55A59_F" "P_55A59_M" "P_60A64" "P_60A64_F" "P_60A64_M" "P_65A69"
[50] "P_65A69_F" "P_65A69_M" "P_70A74" "P_70A74_F" "P_70A74_M" "P_75A79" "P_75A79_F"
[57] "P_75A79_M" "P_80A84" "P_80A84_F" "P_80A84_M" "P_85YMAS" "P_85YMAS_F" "P_85YMAS_M"
[64] "PROM_HNV" "PNACENT" "PNACENT_F" "PNACENT_M" "PNACOE" "PNACOE_F" "PNACOE_M"
[71] "longitude_decimal" "latitude_decimal" "NOM_ENT"
Getting column names that have to do with population age cohorts
(Masculine/Feminine)
matching_columns <- grep("^P_.*[MF]$", column_names, value = TRUE)
matching_columns
[1] "P_0A4_F" "P_0A4_M" "P_5A9_F" "P_5A9_M" "P_10A14_F" "P_10A14_M" "P_15A19_F" "P_15A19_M" "P_20A24_F" "P_20A24_M" "P_25A29_F"
[12] "P_25A29_M" "P_30A34_F" "P_30A34_M" "P_35A39_F" "P_35A39_M" "P_40A44_F" "P_40A44_M" "P_45A49_F" "P_45A49_M" "P_50A54_F" "P_50A54_M"
[23] "P_55A59_F" "P_55A59_M" "P_60A64_F" "P_60A64_M" "P_65A69_F" "P_65A69_M" "P_70A74_F" "P_70A74_M" "P_75A79_F" "P_75A79_M" "P_80A84_F"
[34] "P_80A84_M" "P_85YMAS_F" "P_85YMAS_M"
Separate by sex
ending_in_M <- character(0)
ending_in_F <- character(0)
for (col_name in matching_columns) {
if (endsWith(col_name, "M")) {
ending_in_M <- c(ending_in_M, col_name)
} else if (endsWith(col_name, "F")) {
ending_in_F <- c(ending_in_F, col_name)
}
}
print("Column names ending in M:")
[1] "Column names ending in M:"
print(ending_in_M)
[1] "P_0A4_M" "P_5A9_M" "P_10A14_M" "P_15A19_M" "P_20A24_M" "P_25A29_M" "P_30A34_M" "P_35A39_M" "P_40A44_M" "P_45A49_M" "P_50A54_M"
[12] "P_55A59_M" "P_60A64_M" "P_65A69_M" "P_70A74_M" "P_75A79_M" "P_80A84_M" "P_85YMAS_M"
print("Column names ending in F:")
[1] "Column names ending in F:"
print(ending_in_F)
[1] "P_0A4_F" "P_5A9_F" "P_10A14_F" "P_15A19_F" "P_20A24_F" "P_25A29_F" "P_30A34_F" "P_35A39_F" "P_40A44_F" "P_45A49_F" "P_50A54_F"
[12] "P_55A59_F" "P_60A64_F" "P_65A69_F" "P_70A74_F" "P_75A79_F" "P_80A84_F" "P_85YMAS_F"
cohort_names_m <- c("P_0A4_M",
"P_5A9_M",
"P_10A14_M",
"P_15A19_M",
"P_20A24_M",
"P_25A29_M",
"P_30A34_M",
"P_35A39_M",
"P_40A44_M",
"P_45A49_M",
"P_50A54_M",
"P_55A59_M",
"P_60A64_M",
"P_65A69_M",
"P_70A74_M",
"P_75A79_M",
"P_80A84_M",
"P_85YMAS_M")
cohort_names_f <- c("P_0A4_F",
"P_5A9_F",
"P_10A14_F",
"P_15A19_F",
"P_20A24_F",
"P_25A29_F",
"P_30A34_F",
"P_35A39_F",
"P_40A44_F",
"P_45A49_F",
"P_50A54_F",
"P_55A59_F",
"P_60A64_F",
"P_65A69_F",
"P_70A74_F",
"P_75A79_F",
"P_80A84_F",
"P_85YMAS_F")
municipality <- "Acajete"
locality <- "San Javier"
pueb_norm_filt <- pueb_norm |>
filter(NOM_MUN == municipality, NOM_LOC == locality)
cohort_counts_m <- as.numeric(pueb_norm_filt[1,cohort_names_m])
cohort_counts_f <- as.numeric(pueb_norm_filt[1,cohort_names_f])
data <- tibble(
Cohort = c(cohort_names_m, cohort_names_f),
Count = c(cohort_counts_m, cohort_counts_f),
Sex = rep(c("Male", "Female"), each = length(cohort_names_m))
)
# Plotting population pyramid
ggplot(data, aes(x = reorder(Cohort, -Count), y = Count, fill = Sex)) +
geom_bar(stat = "identity", position = "identity") +
scale_fill_manual(values = c("blue", "pink")) +
coord_flip() +
labs(title = "Population Pyramid",
x = "Population Count",
y = "Age Cohort",
fill = "Sex") +
theme_minimal()

new_ages <- c("0-4",
"5-9",
"10-14",
"15-19",
"20-24",
"25-29",
"30-34",
"35-39",
"40-44",
"45-49",
"50-54",
"55-59",
"60-64",
"65-69",
"70-74",
"75-79",
"80-84",
"85+")
data <- tibble(
Age = paste0(new_ages),
Male = sample(200:1000, length(cohort_names_m), replace = TRUE),
Female = sample(200:1000, length(cohort_names_f), replace = TRUE)
)
data_long <- pivot_longer(
data,
cols = c(Male, Female),
names_to = "Sex",
values_to = "Population"
)
basic_plot <- ggplot(data_long, aes(x = Age, y = ifelse(Sex == "Male", -Population, Population), fill = Sex)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels = abs, limits = max(data_long$Population) * c(-1, 1)) +
coord_flip() +
theme_minimal() +
labs(x = "Age", y = "Population", fill = "Sex", title = "Population Pyramid")
basic_plot

Complete pipeline
census_dataset <- open_dataset(here("data", "processed", "parquet_data_coords"))
pueb_norm <- census_dataset |>
filter(NOM_ENT=="Puebla") |>
collect()
Warning: Invalid metadata$rWarning: Invalid metadata$rWarning: Invalid metadata$rWarning: Invalid metadata$r
municipality <- "Acajete"
locality <- "San Javier"
pueb_norm_filt <- pueb_norm |>
filter(NOM_MUN == municipality, NOM_LOC == locality)
cohort_names_m <- c("P_0A4_M",
"P_5A9_M",
"P_10A14_M",
"P_15A19_M",
"P_20A24_M",
"P_25A29_M",
"P_30A34_M",
"P_35A39_M",
"P_40A44_M",
"P_45A49_M",
"P_50A54_M",
"P_55A59_M",
"P_60A64_M",
"P_65A69_M",
"P_70A74_M",
"P_75A79_M",
"P_80A84_M",
"P_85YMAS_M")
cohort_names_f <- c("P_0A4_F",
"P_5A9_F",
"P_10A14_F",
"P_15A19_F",
"P_20A24_F",
"P_25A29_F",
"P_30A34_F",
"P_35A39_F",
"P_40A44_F",
"P_45A49_F",
"P_50A54_F",
"P_55A59_F",
"P_60A64_F",
"P_65A69_F",
"P_70A74_F",
"P_75A79_F",
"P_80A84_F",
"P_85YMAS_F")
new_ages <- c("0-4",
"5-9",
"10-14",
"15-19",
"20-24",
"25-29",
"30-34",
"35-39",
"40-44",
"45-49",
"50-54",
"55-59",
"60-64",
"65-69",
"70-74",
"75-79",
"80-84",
"85+")
data <- tibble(
Age = paste0(new_ages),
Male = as.numeric(pueb_norm_filt[1,cohort_names_m]),
Female = as.numeric(pueb_norm_filt[1,cohort_names_f])
)
data_long <- pivot_longer(
data,
cols = c(Male, Female),
names_to = "Sex",
values_to = "Population"
)
basic_plot <- ggplot(data_long, aes(x = Age, y = ifelse(Sex == "Male", -Population, Population), fill = Sex)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels = abs, limits = max(data_long$Population) * c(-1, 1)) +
coord_flip() +
theme_minimal() +
labs(x = "Age", y = "Population", fill = "Sex", title = "Population Pyramid")
basic_plot

Card
card <- census_dataset |>
filter(
NOM_ENT == "Puebla",
NOM_MUN == "Acateno",
NOM_LOC == "Santa Andrea"
) |>
collect()
Warning: Invalid metadata$rWarning: Invalid metadata$rWarning: Invalid metadata$rWarning: Invalid metadata$r
total_population <- as.numeric(card[1, c("POBTOT")], na.rm = TRUE)
paste("Total Population:", total_population)
[1] "Total Population: 1"
Pie
tot <- census_dataset |>
filter(NOM_ENT=="Total nacional") |>
collect()
Warning: Invalid metadata$rWarning: Invalid metadata$rWarning: Invalid metadata$rWarning: Invalid metadata$r
tot
# origin <- census_dataset |>
# filter(
# NOM_ENT == "Puebla",
# NOM_MUN == "Acateno",
# NOM_LOC == "Santa Andrea"
# ) |>
# collect()
tot
# Extract birth data
birth_local <- as.numeric(tot[1, "PNACENT"])
birth_another <- as.numeric(tot[1, "PNACOE"])
# Debugging output
print(paste("Birth Local:", birth_local))
[1] "Birth Local: 102724322"
print(paste("Birth Another:", birth_another))
[1] "Birth Another: 21611963"
# Create ratio dataframe
ratio_df <- tibble(
Category = c("Local", "Other"),
Ratio = c(birth_local, birth_another)
)
# Calculate percentages
ratio_df$Percentage <- ratio_df$Ratio / sum(ratio_df$Ratio) * 100
# Plot the pie chart
gg <- ggplot(ratio_df, aes(x = "", y = Ratio, fill = Category)) +
geom_bar(stat = "identity", width = 1) +
coord_polar(theta = "y") +
theme_void() +
theme(legend.position = "bottom") +
scale_fill_manual(values = c("#00BFC4", "#F8766D")) +
geom_text(aes(label = paste0(round(Percentage), "%")),
position = position_stack(vjust = 0.5),
size = 5, color = "white", fontface = "bold")
gg

NA
Read CSV from URL
test_csv <- read.csv("https://raw.github.ubc.ca/MDS-2023-24/DSCI_532_individual-assignment_marcony1/master/data/processed/data_coords.csv?token=GHSAT0AAAAAAAAACLO5WK36WJ6S74E3NU4CZRJJJGA")
test_csv
NA
filtered_data <- filter(test_csv, NOM_ENT == "Puebla")
filtered_data
Download with curl
library(curl)
# Define the URL of the CSV file
url <- "https://raw.github.ubc.ca/MDS-2023-24/DSCI_532_individual-assignment_marcony1/master/data/processed/data_coords.csv?token=GHSAT0AAAAAAAAACLO5WK36WJ6S74E3NU4CZRJJJGA"
response <- curl::curl_fetch_memory(url)
if (response$status_code == 200) {
csv_content <- rawToChar(response$content)
test_csv <- read.csv(text = csv_content)
print(test_csv)
}
library(RCurl)
x <- getURL("https://raw.github.ubc.ca/MDS-2023-24/DSCI_532_individual-assignment_marcony1/master/data/processed/data_coords.csv?token=GHSAT0AAAAAAAAACLO5WK36WJ6S74E3NU4CZRJJJGA")
y <- read.csv(text = x)
y
test_2 <- arrow::read_parquet("https://github.ubc.ca/MDS-2023-24/DSCI_532_individual-assignment_marcony1/tree/master/data/processed/parquet_data_coords.parquet")
Error: Invalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file.
---
title: "R Notebook"
output: html_notebook
author: Marco Polo Bravo Montiel
date: 2020-04-23
---

# Shiny App Experiment Lab

### Libraries

```{r}
library(here)
library(dplyr)
library(arrow)
library(tidyr)
library(geojsonio)
```

### Reading data

```{r}
census_dataset <- open_dataset(here("data", "processed", "parquet_data_coords"))
census_dataset

```

### Reading specific data

```{r}
pueb_norm <- census_dataset |>
    filter(NOM_ENT=="Puebla") |> 
    collect()

pueb_norm
```

```{r}
unique(pueb_norm$NOM_MUN)
```

### Example

```{r}
extract_coordinates <- function(data, municipality, locality) {
  selected_location <- data |> 
    filter(NOM_MUN == municipality, NOM_LOC == locality)
  
  coordinates <- tibble(
    long = selected_location$longitude_decimal,
    lat = selected_location$latitude_decimal
  )
  
  return(coordinates)
}


municipality <- "Acajete"
locality <- "Santa Isabel Tepetzala"

red_point <- extract_coordinates(pueb_norm, municipality, locality) |> 
  slice(1)

# red_point <- data.frame(long = -98.2035, lat = 19.0414)
geojson_file <- geojson_read("../data/processed/mexico.geojson",  what = "sp")

filtered_geojson <- geojson_file |> 
  filter(name == "Puebla")

ggplot() +
  geom_polygon(data = filtered_geojson,
               aes(x = long, y = lat, group = group),
               fill = "lightgray", color = "white") +
  geom_point(data = red_point, aes(x = long, y = lat), color = "red", size = 3) +
  theme_void() +
  coord_map()
```

### Population graph

```{r}
pueb_norm
```

### Getting column names

```{r}
column_names <- names(pueb_norm)
column_names
```

### Getting column names that have to do with population age cohorts (Masculine/Feminine)

```{r}
matching_columns <- grep("^P_.*[MF]$", column_names, value = TRUE)
matching_columns
```

### Separate by sex

```{r}
ending_in_M <- character(0)
ending_in_F <- character(0)

for (col_name in matching_columns) {
  if (endsWith(col_name, "M")) {
    ending_in_M <- c(ending_in_M, col_name)
  } else if (endsWith(col_name, "F")) {
    ending_in_F <- c(ending_in_F, col_name)
  }
}

print("Column names ending in M:")
print(ending_in_M)

print("Column names ending in F:")
print(ending_in_F)
```

```{r}
cohort_names_m <- c("P_0A4_M",
                    "P_5A9_M",
                    "P_10A14_M",
                    "P_15A19_M",
                    "P_20A24_M",
                    "P_25A29_M",
                    "P_30A34_M",
                    "P_35A39_M",
                    "P_40A44_M",
                    "P_45A49_M",
                    "P_50A54_M",
                    "P_55A59_M",
                    "P_60A64_M",
                    "P_65A69_M",
                    "P_70A74_M",
                    "P_75A79_M",
                    "P_80A84_M",
                    "P_85YMAS_M")

cohort_names_f <- c("P_0A4_F",
                    "P_5A9_F",
                    "P_10A14_F",
                    "P_15A19_F",
                    "P_20A24_F",
                    "P_25A29_F",
                    "P_30A34_F",
                    "P_35A39_F",
                    "P_40A44_F",
                    "P_45A49_F",
                    "P_50A54_F",
                    "P_55A59_F",
                    "P_60A64_F",
                    "P_65A69_F", 
                    "P_70A74_F",
                    "P_75A79_F",
                    "P_80A84_F", 
                    "P_85YMAS_F")



municipality <- "Acajete"
locality <- "San Javier"

pueb_norm_filt <- pueb_norm |> 
    filter(NOM_MUN == municipality, NOM_LOC == locality)

cohort_counts_m <- as.numeric(pueb_norm_filt[1,cohort_names_m])
cohort_counts_f <- as.numeric(pueb_norm_filt[1,cohort_names_f])

data <- tibble(
  Cohort = c(cohort_names_m, cohort_names_f),
  Count = c(cohort_counts_m, cohort_counts_f),
  Sex = rep(c("Male", "Female"), each = length(cohort_names_m))
)

# Plotting population pyramid
ggplot(data, aes(x = reorder(Cohort, -Count), y = Count, fill = Sex)) +
  geom_bar(stat = "identity", position = "identity") +
  scale_fill_manual(values = c("blue", "pink")) +  
  coord_flip() +  
  labs(title = "Population Pyramid",
       x = "Population Count",
       y = "Age Cohort",
       fill = "Sex") +
  theme_minimal()  

```

```{r}
new_ages <- c("0-4",
                 "5-9",
                 "10-14",
                 "15-19",
                 "20-24",
                 "25-29", 
                 "30-34", 
                 "35-39", 
                 "40-44", 
                 "45-49",
                 "50-54",
                 "55-59",
                 "60-64", 
                 "65-69",
                 "70-74", 
                 "75-79", 
                 "80-84", 
                 "85+")

data <- tibble(
  Age = paste0(new_ages),
  Male = sample(200:1000, length(cohort_names_m), replace = TRUE),
  Female = sample(200:1000, length(cohort_names_f), replace = TRUE)
)

data_long <- pivot_longer(
  data, 
  cols = c(Male, Female), 
  names_to = "Sex", 
  values_to = "Population"
)

basic_plot <- ggplot(data_long, aes(x = Age, y = ifelse(Sex == "Male", -Population, Population), fill = Sex)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = abs, limits = max(data_long$Population) * c(-1, 1)) +
  coord_flip() +
  theme_minimal() +
  labs(x = "Age", y = "Population", fill = "Sex", title = "Population Pyramid")

basic_plot
```

### Complete pipeline

```{r}
census_dataset <- open_dataset(here("data", "processed", "parquet_data_coords"))


pueb_norm <- census_dataset |>
    filter(NOM_ENT=="Puebla") |> 
    collect()

municipality <- "Acajete"
locality <- "San Javier"

pueb_norm_filt <- pueb_norm |> 
    filter(NOM_MUN == municipality, NOM_LOC == locality)


cohort_names_m <- c("P_0A4_M",
                    "P_5A9_M",
                    "P_10A14_M",
                    "P_15A19_M",
                    "P_20A24_M",
                    "P_25A29_M",
                    "P_30A34_M",
                    "P_35A39_M",
                    "P_40A44_M",
                    "P_45A49_M",
                    "P_50A54_M",
                    "P_55A59_M",
                    "P_60A64_M",
                    "P_65A69_M",
                    "P_70A74_M",
                    "P_75A79_M",
                    "P_80A84_M",
                    "P_85YMAS_M")

cohort_names_f <- c("P_0A4_F",
                    "P_5A9_F",
                    "P_10A14_F",
                    "P_15A19_F",
                    "P_20A24_F",
                    "P_25A29_F",
                    "P_30A34_F",
                    "P_35A39_F",
                    "P_40A44_F",
                    "P_45A49_F",
                    "P_50A54_F",
                    "P_55A59_F",
                    "P_60A64_F",
                    "P_65A69_F", 
                    "P_70A74_F",
                    "P_75A79_F",
                    "P_80A84_F", 
                    "P_85YMAS_F")



new_ages <- c("0-4",
                 "5-9",
                 "10-14",
                 "15-19",
                 "20-24",
                 "25-29", 
                 "30-34", 
                 "35-39", 
                 "40-44", 
                 "45-49",
                 "50-54",
                 "55-59",
                 "60-64", 
                 "65-69",
                 "70-74", 
                 "75-79", 
                 "80-84", 
                 "85+")


data <- tibble(
  Age = paste0(new_ages),
  Male = as.numeric(pueb_norm_filt[1,cohort_names_m]),
  Female = as.numeric(pueb_norm_filt[1,cohort_names_f])
)

data_long <- pivot_longer(
  data, 
  cols = c(Male, Female), 
  names_to = "Sex", 
  values_to = "Population"
)
basic_plot <- ggplot(data_long, aes(x = Age, y = ifelse(Sex == "Male", -Population, Population), fill = Sex)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = abs, limits = max(data_long$Population) * c(-1, 1)) +
  coord_flip() +
  theme_minimal() +
  labs(x = "Age", y = "Population", fill = "Sex", title = "Population Pyramid")

basic_plot
```

### Card

```{r}
card <- census_dataset |>
    filter(
      NOM_ENT == "Puebla",
      NOM_MUN == "Acateno",
      NOM_LOC == "Santa Andrea"
    ) |>
  collect()
    
    total_population <- as.numeric(card[1, c("POBTOT")], na.rm = TRUE)
    paste("Total Population:", total_population)
```

### Pie

```{r}
tot <- census_dataset |>
    filter(NOM_ENT=="Total nacional") |> 
    collect()

tot
```

```{r}
# origin <- census_dataset |>  
#      filter(
#       NOM_ENT == "Puebla",
#       NOM_MUN == "Acateno",
#       NOM_LOC == "Santa Andrea"
#     ) |>
#   collect()
    
tot
    
    # Extract birth data
    birth_local <- as.numeric(tot[1, "PNACENT"])
    birth_another <- as.numeric(tot[1, "PNACOE"])
    
    # Debugging output
    print(paste("Birth Local:", birth_local))
    print(paste("Birth Another:", birth_another))
    
    # Create ratio dataframe
    ratio_df <- tibble(
      Category = c("Local", "Other"),
      Ratio = c(birth_local, birth_another)
    )
    
    # Calculate percentages
    ratio_df$Percentage <- ratio_df$Ratio / sum(ratio_df$Ratio) * 100
    
    # Plot the pie chart
    gg <- ggplot(ratio_df, aes(x = "", y = Ratio, fill = Category)) +
      geom_bar(stat = "identity", width = 1) +
      coord_polar(theta = "y") +
      theme_void() +
      theme(legend.position = "bottom") +
      scale_fill_manual(values = c("#00BFC4", "#F8766D")) +
      geom_text(aes(label = paste0(round(Percentage), "%")), 
                position = position_stack(vjust = 0.5),
                size = 5, color = "white", fontface = "bold")
    
    gg
    
```

### Read CSV from URL

```{r}
test_csv <- read.csv("https://raw.github.ubc.ca/MDS-2023-24/DSCI_532_individual-assignment_marcony1/master/data/processed/data_coords.csv?token=GHSAT0AAAAAAAAACLO5WK36WJ6S74E3NU4CZRJJJGA")

test_csv

```

```{r}
filtered_data <- filter(test_csv, NOM_ENT == "Puebla")
filtered_data
```

### Download with curl

```{r}
library(curl)

url <- "https://raw.github.ubc.ca/MDS-2023-24/DSCI_532_individual-assignment_marcony1/master/data/processed/data_coords.csv?token=GHSAT0AAAAAAAAACLO5WK36WJ6S74E3NU4CZRJJJGA"

response <- curl::curl_fetch_memory(url)

if (response$status_code == 200) {
  csv_content <- rawToChar(response$content)
    test_csv <- read.csv(text = csv_content)
print(test_csv)
}
```

```{r}
library(RCurl)
x <- getURL("https://raw.github.ubc.ca/MDS-2023-24/DSCI_532_individual-assignment_marcony1/master/data/processed/data_coords.csv?token=GHSAT0AAAAAAAAACLO5WK36WJ6S74E3NU4CZRJJJGA")
y <- read.csv(text = x)
y
```

```{r}
# test_2 <- arrow::read_parquet("https://github.ubc.ca/MDS-2023-24/DSCI_532_individual-assignment_marcony1/tree/master/data/processed/parquet_data_coords.parquet")
```
